In this project, Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras (Tensorflow) with Gensim to create word2vec and TF-IDF were used respectively to classify SMS as an either spam or ham.
In this project, you are expected to learn a Machine Learning model that classifies a given line as belonging to one of the following 12 novels:
- alice_in_wonderland
- dracula
- dubliners
- great_expectations
- hard_times
- huckleberry_finn
- les_miserable
- moby_dick
- oliver_twist
- peter_pan
- talw_of_two_cities
- tom_sawyer
Deeplearing (LSTM) on top of Keras (Tensorflow) is performing the novel corpus data to solve this problem
after creating word2vec by using Gensim.
In this project, we tried to solve imbalanced data using over/under resampling techniques
In this project, we applied time series decomposition techniques and random forest algorithm to build a ML model
In this project, a ML micro-service was developed by using REST and Docker after building a ML model using random forest algoritm
In this project, two different data set which have location based (GPS) feature were joined Kd-tree.